05208nas a2200541 4500008004100000022001400041245012300055210006900178260001500247300001300262490000600275520365800281653001003939653000903949653002003958653002803978653001904006653002404025653001104049653001104060653002504071653000904096653001604105653003204121653002404153653002004177653001704197100001704214700002104231700002004252700002704272700002204299700002304321700001704344700002404361700002304385700002304408700001804431700002304449700002704472700002104499700001904520700002704539700002104566700002304587700002004610856003604630 2020 eng d a2574-380500aPerformance of the Pooled Cohort Equations to Estimate Atherosclerotic Cardiovascular Disease Risk by Body Mass Index.0 aPerformance of the Pooled Cohort Equations to Estimate Atheroscl c2020 10 01 ae20232420 v33 a

Importance: Obesity is a global health challenge and a risk factor for atherosclerotic cardiovascular disease (ASVCD). Performance of the pooled cohort equations (PCE) for ASCVD risk by body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) is unknown.

Objective: To assess performance of the PCE across clinical BMI categories.

Design, Setting, and Participants: This cohort study used pooled individual-level data from 8 community-based, prospective, longitudinal cohort studies with 10-year ASCVD event follow-up from 1996 to 2016. We included all adults ages 40 to 79 years without baseline ASCVD or statin use, resulting in a sample size of 37 311 participants. Data were analyzed from August 2017 to July 2020.

Exposures: Participant BMI category: underweight (<18.5), normal weight (18.5 to <25), overweight (25 to <30), mild obesity (30 to <35), and moderate to severe obesity (≥35).

Main Outcomes and Measures: Discrimination (Harrell C statistic) and calibration (Nam-D'Agostino χ2 goodness-of-fit test) of the PCE across BMI categories. Improvement in discrimination and net reclassification with addition of BMI, waist circumference, and high-sensitivity C-reactive protein (hsCRP) to the PCE.

Results: Among 37 311 participants (mean [SD] age, 58.6 [11.8] years; 21 897 [58.7%] women), 380 604 person-years of follow-up were conducted. Mean (SD) baseline BMI was 29.0 (6.2), and 360 individuals (1.0%) were in the underweight category, 9937 individuals (26.6%) were in the normal weight category, 13 601 individuals (36.4%) were in the overweight category, 7783 individuals (20.9%) were in the mild obesity category, and 5630 individuals (15.1%) were in the moderate to severe obesity category. Median (interquartile range [IQR]) 10-year estimated ASCVD risk was 7.1% (2.5%-15.4%), and 3709 individuals (9.9%) developed ASCVD over a median (IQR) 10.8 [8.5-12.6] years. The PCE overestimated ASCVD risk in the overall cohort (estimated/observed [E/O] risk ratio, 1.22; 95% CI, 1.18-1.26) and across all BMI categories except the underweight category. Calibration was better near the clinical decision threshold in all BMI groups but worse among individuals with moderate or severe obesity (E/O risk ratio, 1.36; 95% CI, 1.25-1.47) and among those with the highest estimated ASCVD risk ≥20%. The PCE C statistic overall was 0.760 (95% CI, 0.753-0.767), with lower discrimination in the moderate or severe obesity group (C statistic, 0.742; 95% CI, 0.721-0.763) compared with the normal-range BMI group (C statistic, 0.785; 95% CI, 0.772-0.798). Waist circumference (hazard ratio, 1.07 per 1-SD increase; 95% CI, 1.03-1.11) and hsCRP (hazard ratio, 1.07 per 1-SD increase; 95% CI, 1.05-1.09), but not BMI, were associated with increased ASCVD risk when added to the PCE. However, these factors did not improve model performance (C statistic, 0.760; 95% CI, 0.753-0.767) with or without added metrics.

Conclusions and Relevance: These findings suggest that the PCE had acceptable model discrimination and were well calibrated at clinical decision thresholds but overestimated risk of ASCVD for individuals in overweight and obese categories, particularly individuals with high estimated risk. Incorporation of the usual clinical measures of obesity did not improve risk estimation of the PCE. Future research is needed to determine whether incorporation of alternative high-risk obesity markers (eg, weight trajectory or measures of visceral or ectopic fat) into the PCE may improve risk prediction.

10aAdult10aAged10aBody Mass Index10aCardiovascular Diseases10aCohort Studies10aCorrelation of Data10aFemale10aHumans10aLongitudinal Studies10aMale10aMiddle Aged10aProportional Hazards Models10aProspective Studies10aRisk Assessment10aRisk Factors1 aKhera, Rohan1 aPandey, Ambarish1 aAyers, Colby, R1 aCarnethon, Mercedes, R1 aGreenland, Philip1 aNdumele, Chiadi, E1 aNambi, Vijay1 aSeliger, Stephen, L1 aChaves, Paulo, H M1 aSafford, Monika, M1 aCushman, Mary1 aXanthakis, Vanessa1 aVasan, Ramachandran, S1 aMentz, Robert, J1 aCorrea, Adolfo1 aLloyd-Jones, Donald, M1 aBerry, Jarett, D1 ade Lemos, James, A1 aNeeland, Ian, J uhttps://chs-nhlbi.org/node/862604763nas a2200649 4500008004100000022001400041245008600055210006900141260001600210300001200226490000800238520284000246653001003086653000903096653001303105653003103118653002003149653002803169653001503197653001103212653002403223653001803247653001103265653000903276653001603285653002003301653001703321100002003338700002503358700001903383700002403402700002103426700002303447700002203470700002103492700002403513700002303537700001603560700002603576700002003602700002403622700002203646700002703668700002403695700002603719700002303745700002103768700002203789700002003811700002003831700002303851700002403874700002303898700001803921710013803939856003604077 2024 eng d a1524-453900aDevelopment and Validation of the American Heart Association's PREVENT Equations.0 aDevelopment and Validation of the American Heart Associations PR c2024 Feb 06 a430-4490 v1493 a

BACKGROUND: Multivariable equations are recommended by primary prevention guidelines to assess absolute risk of cardiovascular disease (CVD). However, current equations have several limitations. Therefore, we developed and validated the American Heart Association Predicting Risk of CVD EVENTs (PREVENT) equations among US adults 30 to 79 years of age without known CVD.

METHODS: The derivation sample included individual-level participant data from 25 data sets (N=3 281 919) between 1992 and 2017. The primary outcome was CVD (atherosclerotic CVD and heart failure). Predictors included traditional risk factors (smoking status, systolic blood pressure, cholesterol, antihypertensive or statin use, and diabetes) and estimated glomerular filtration rate. Models were sex-specific, race-free, developed on the age scale, and adjusted for competing risk of non-CVD death. Analyses were conducted in each data set and meta-analyzed. Discrimination was assessed using the Harrell C-statistic. Calibration was calculated as the slope of the observed versus predicted risk by decile. Additional equations to predict each CVD subtype (atherosclerotic CVD and heart failure) and include optional predictors (urine albumin-to-creatinine ratio and hemoglobin A1c), and social deprivation index were also developed. External validation was performed in 3 330 085 participants from 21 additional data sets.

RESULTS: Among 6 612 004 adults included, mean±SD age was 53±12 years, and 56% were women. Over a mean±SD follow-up of 4.8±3.1 years, there were 211 515 incident total CVD events. The median C-statistics in external validation for CVD were 0.794 (interquartile interval, 0.763-0.809) in female and 0.757 (0.727-0.778) in male participants. The calibration slopes were 1.03 (interquartile interval, 0.81-1.16) and 0.94 (0.81-1.13) among female and male participants, respectively. Similar estimates for discrimination and calibration were observed for atherosclerotic CVD- and heart failure-specific models. The improvement in discrimination was small but statistically significant when urine albumin-to-creatinine ratio, hemoglobin A1c, and social deprivation index were added together to the base model to total CVD (ΔC-statistic [interquartile interval] 0.004 [0.004-0.005] and 0.005 [0.004-0.007] among female and male participants, respectively). Calibration improved significantly when the urine albumin-to-creatinine ratio was added to the base model among those with marked albuminuria (>300 mg/g; 1.05 [0.84-1.20] versus 1.39 [1.14-1.65]; =0.01).

CONCLUSIONS: PREVENT equations accurately and precisely predicted risk for incident CVD and CVD subtypes in a large, diverse, and contemporary sample of US adults by using routinely available clinical variables.

10aAdult10aAged10aAlbumins10aAmerican Heart Association10aAtherosclerosis10aCardiovascular Diseases10aCreatinine10aFemale10aGlycated Hemoglobin10aHeart Failure10aHumans10aMale10aMiddle Aged10aRisk Assessment10aRisk Factors1 aKhan, Sadiya, S1 aMatsushita, Kunihiro1 aSang, Yingying1 aBallew, Shoshana, H1 aGrams, Morgan, E1 aSurapaneni, Aditya1 aBlaha, Michael, J1 aCarson, April, P1 aChang, Alexander, R1 aCiemins, Elizabeth1 aGo, Alan, S1 aGutierrez, Orlando, M1 aHwang, Shih-Jen1 aJassal, Simerjot, K1 aKovesdy, Csaba, P1 aLloyd-Jones, Donald, M1 aShlipak, Michael, G1 aPalaniappan, Latha, P1 aSperling, Laurence1 aVirani, Salim, S1 aTuttle, Katherine1 aNeeland, Ian, J1 aChow, Sheryl, L1 aRangaswami, Janani1 aPencina, Michael, J1 aNdumele, Chiadi, E1 aCoresh, Josef1 aChronic Kidney Disease Prognosis Consortium and the American Heart Association Cardiovascular-Kidney-Metabolic Science Advisory Group uhttps://chs-nhlbi.org/node/963704295nas a2200529 4500008004100000022001400041245009000055210006900145260001600214300001200230490000600242520289400248653000903142653001503151653001103166653001203177653001803189653001103207653001403218653000903232653001603241653001503257653001703272100002103289700002003310700002103330700001603351700002303367700002303390700002203413700001903435700002303454700002003477700003103497700001903528700002003547700002003567700002003587700002203607700001603629700001803645700001703663700001803680700001303698700001803711856003603729 2024 eng d a2380-659100aHigh Throughput Plasma Proteomics and Risk of Heart Failure and Frailty in Late Life.0 aHigh Throughput Plasma Proteomics and Risk of Heart Failure and  c2024 Jul 01 a649-6580 v93 a

IMPORTANCE: Heart failure (HF) and frailty frequently coexist and may share a common pathobiology, although the underlying mechanisms remain unclear. Understanding these mechanisms may provide guidance for preventing and treating both conditions.

OBJECTIVE: To identify shared pathways between incident HF and frailty in late life using large-scale proteomics.

DESIGN, SETTING, AND PARTICIPANTS: In this cohort study, 4877 aptamers (Somascan v4) were measured among participants in the community-based longitudinal Atherosclerosis Risk In Communities (ARIC) cohort study at visit 3 (V3; 1993-1995; n = 10 638) and at visit 5 (V5; 2011-2013; n = 3908). Analyses were externally replicated among 3189 participants in the Cardiovascular Health Study (CHS). Data analysis was conducted from February 2022 to June 2023.

EXPOSURES: Protein aptamers, measured at study V3 and V5.

MAIN OUTCOMES AND MEASURES: Outcomes assessed included incident HF hospitalization after V3 and after V5, prevalent frailty at V5, and incident frailty between V5 and visit 6 (V6; 2016-2017; n = 4131). Frailty was assessed using the Fried criteria. Analyses were adjusted for age, gender, race, field center, hypertension, diabetes, smoking status, body mass index, estimated glomerular filtration rate, prevalent coronary heart disease, prevalent atrial fibrillation, and history of myocardial infarction. Mendelian randomization (MR) analysis was performed to assess potential causal effects of candidate proteins on HF and frailty.

RESULTS: A total of 4877 protein aptamers were measured among 10 638 participants at V3 (mean [SD] age, 60 [6] years; 4886 [46%] men). Overall, 286 proteins were associated with incident HF after V3 (822 events; P < 1.0 × 10-5), 83 of which were also associated with incident after V5 (336 events; P < 1.7 × 10-4). Among HF-free participants at V5 (n = 3908; mean [SD] age, 75 [5] years; 1861 [42%] men), 48 of 83 HF-associated proteins were associated with prevalent frailty (223 cases; P < 6.0 × 10-4), 18 of which were also associated with incident frailty at V6 (152 cases; P < 1.0 × 10-3). These proteins enriched fibrosis and inflammation pathways and demonstrated stronger associations with incident HF with preserved ejection fraction (HFpEF) than HF with reduced ejection fraction. All 18 proteins were associated with both prevalent frailty and incident HF in CHS. MR identified potential causal effects of several proteins on frailty and HF.

CONCLUSIONS AND RELEVANCE: In this study, the proteins associated with risk of HF and frailty enrich for pathways related to inflammation and fibrosis as well as risk of HFpEF. Several of these proteins could potentially contribute to the shared pathophysiology of frailty and HF.

10aAged10aBiomarkers10aFemale10aFrailty10aHeart Failure10aHumans10aIncidence10aMale10aMiddle Aged10aProteomics10aRisk Factors1 aRamonfaur, Diego1 aBuckley, Leo, F1 aArthur, Victoria1 aYang, Yimin1 aClaggett, Brian, L1 aNdumele, Chiadi, E1 aWalker, Keenan, A1 aAustin, Thomas1 aOdden, Michelle, C1 aFloyd, James, S1 avan Wijk, Sandra, Sanders-1 aNjoroge, Joyce1 aKizer, Jorge, R1 aKitzman, Dalane1 aKonety, Suma, H1 aSchrack, Jennifer1 aLiu, Fangyu1 aWindham, Gwen1 aPalta, Priya1 aCoresh, Josef1 aYu, Bing1 aShah, Amil, M uhttps://chs-nhlbi.org/node/9705